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day2021_10——30

本文主要是介绍day2021_10——30,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!

今日内容

  • Jupyter

    image

    在此处摁下“m"键就会编程markdown模式
    image

    摁”b“键是在下方创建代码

    image

  • %matplotlib inline

    作用:%matplotlib inline这一句是IPython的魔法函数,可以在IPython编译器里直接使用,作用是内嵌画图,省略掉plt.show()这一步,直接显示图像。

    如果不加这一句的话,我们在画图结束之后需要加上plt.show()才可以显示图像。不然就只有<matplotlib.collections.PathCollection at 0x2aa7c1ae490>这行话

    image

    image

一定导入的是matplotlib.pyplot 不然里面没有scatter方法,

scatter()方法的参数含义参考:https://blog.csdn.net/m0_37393514/article/details/81298503

  • 具体代码实现如下
import pandas as pd
data = pd.read_csv("C:\\Users\\94823\\Desktop\\aaa.csv")
data
aaaIncome Education Income
0 1 10.000000 26.658839
1 2 10.401338 27.306435
2 3 10.842809 22.132410
3 4 11.244147 21.169841
4 5 11.645449 15.192634
5 6 12.086957 26.398951
6 7 12.048829 17.435307
7 8 12.889632 25.507885
8 9 13.290970 36.884595
9 10 13.732441 39.666109
10 11 14.133779 34.396281
11 12 14.635117 41.497994
12 13 14.978589 44.981575
13 14 15.377926 47.039595
14 15 15.779264 48.252578
15 16 16.220736 57.034251
16 17 16.622074 51.490919
17 18 17.023411 51.336621
18 19 17.464883 57.681998
19 20 17.866221 68.553714
20 21 18.267559 64.310925
21 22 18.709030 68.959009
22 23 19.110368 74.614639
23 24 19.511706 71.867195
24 25 19.913043 76.098135
25 26 20.354515 75.775216
26 27 20.755853 72.486055
27 28 21.167191 77.355021
28 29 21.598662 72.118790
29 30 22.000000 80.260571
import matplotlib.pyplot as plt
%matplotlib inline
plt.scatter(data.Education,data.Income)
<matplotlib.collections.PathCollection at 0x2aa7c1ae490>

image

x = data.Education  # 定义横纵坐标
y = data.Income
import tensorflow as tf
model = tf.keras.Sequential()  # 建立一个输入一个输出的模型。比如图中的线性模型y=ax+b,x为一个输入,y为一个输出
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))  
    # 前面的1对应的是units这个参数,表示该层有多少个神经单元。
    # 同时也是与上一层连接的W的列数。
    # input_shape的第一维一般表示一个batch的大小,
    # 即有多少个样本。后面表示每个样本应该有多少特征,也是多少个列。如果不写会根据输入的X自动推断。
    # 具体参考https://blog.csdn.net/weixin_42055967/article/details/104420985
model.summary()  # ax+b param中的两个参数就是a与b
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 1)                 2         
=================================================================
Total params: 2
Trainable params: 2
Non-trainable params: 0
_________________________________________________________________
model.compile(
    optimizer = 'adam',
    loss = 'mse'
)  # 减少误差优化的方法是梯度下降法:简写‘adam’,优化的损失函数是均方差‘mse’,
  # 通过这种方法编译
history = model.fit(x, y, epochs=5000)  # 把所有数据训练5000次
Epoch 4992/5000
1/1 [==============================] - 0s 3ms/step - loss: 287.1974
Epoch 4993/5000
1/1 [==============================] - 0s 2ms/step - loss: 286.9698
Epoch 4994/5000
1/1 [==============================] - 0s 3ms/step - loss: 286.7423
Epoch 4995/5000
1/1 [==============================] - 0s 3ms/step - loss: 286.5152
Epoch 4996/5000
1/1 [==============================] - 0s 2ms/step - loss: 286.2881
Epoch 4997/5000
1/1 [==============================] - 0s 2ms/step - loss: 286.0614
Epoch 4998/5000
1/1 [==============================] - 0s 3ms/step - loss: 285.8348
Epoch 4999/5000
1/1 [==============================] - 0s 3ms/step - loss: 285.6085
Epoch 5000/5000
1/1 [==============================] - 0s 3ms/step - loss: 285.3824
model.predict(x)  # 通过x预测
array([[25.934877],
       [26.824005],
       [27.802044],
       [28.691174],
       [29.58022 ],
       [30.558342],
       [30.473873],
       [32.336597],
       [33.225723],
       [34.203762],
       [35.092888],
       [36.20356 ],
       [36.96449 ],
       [37.849186],
       [38.73831 ],
       [39.716354],
       [40.60548 ],
       [41.494606],
       [42.47265 ],
       [43.361774],
       [44.250904],
       [45.228943],
       [46.118073],
       [47.007195],
       [47.89632 ],
       [48.874363],
       [49.763493],
       [50.674774],
       [51.630657],
       [52.519787]], dtype=float32)
model.predict(pd.Series([20]))
array([[48.088966]], dtype=float32)

明日计划

  • 继续学习tensorflow
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